# This file covers the code in CovariateData.R. View coverage for this file using
# library(testthat); library(FeatureExtraction)
# covr::file_report(covr::file_coverage("R/CovariateData.R", "tests/testthat/test-CovariateData.R"))

test_that("test CovariateData Class on Empty", {
  skip_if_not(dbms == "sqlite")
  # create 4 scenarios of Covariate Data
  # 1) error (non class), 2) covariate data, 3) aggregatedCovariate Data,
  # 4) temporalCovariate Data

  errCovData <- list()

  covData <- FeatureExtraction::createEmptyCovariateData(
    cohortIds = 9999,
    aggregated = FALSE,
    temporal = FALSE
  )
  aggCovData <- FeatureExtraction::createEmptyCovariateData(
    cohortIds = 9999,
    aggregated = TRUE,
    temporal = FALSE
  )

  tempCovData <- FeatureExtraction::createEmptyCovariateData(
    cohortIds = 9999,
    aggregated = FALSE,
    temporal = TRUE
  )

  # check that objects are covariate Data class
  expect_false(isCovariateData(errCovData))
  expect_true(isCovariateData(covData))
  expect_true(isCovariateData(aggCovData))
  expect_true(isCovariateData(tempCovData))

  # check that objects are aggregate covariate data class
  expect_error(isAggregatedCovariateData(errCovData))
  expect_false(isAggregatedCovariateData(covData))
  expect_true(isAggregatedCovariateData(aggCovData))
  expect_false(isAggregatedCovariateData(tempCovData))


  # check that objects are temporal covariate data class
  expect_error(isTemporalCovariateData(errCovData))
  expect_false(isTemporalCovariateData(covData))
  expect_false(isTemporalCovariateData(aggCovData))
  expect_true(isTemporalCovariateData(tempCovData))

  Andromeda::close(covData)
  Andromeda::close(aggCovData)
  Andromeda::close(tempCovData)
})

test_that("test saveCovariateData error cases", {
  skip_on_cran()
  skip_if_not(dbms == "sqlite" && exists("eunomiaConnection"))
  saveFileTest <- tempfile("covDatSave")
  settings <- createDefaultCovariateSettings()
  covariateData <- getDbCovariateData(
    connectionDetails = eunomiaConnectionDetails,
    cdmDatabaseSchema = eunomiaCdmDatabaseSchema,
    cohortDatabaseSchema = eunomiaOhdsiDatabaseSchema,
    cohortIds = c(1),
    covariateSettings = settings,
    aggregated = FALSE
  )
  # create error for test
  errCovData <- list()

  expect_error(saveCovariateData()) # empty call error
  expect_error(saveCovariateData(covariateData)) # no file error
  expect_error(saveCovariateData(errCovData, file = saveFileTest)) # non covariateData class error
  expect_message(
    saveCovariateData(covariateData, file = saveFileTest),
    "Disconnected Andromeda. This data object can no longer be used"
  )
  Andromeda::close(covariateData)
  unlink(saveFileTest)
})

test_that("test summary call for covariateData class", {
  skip_if_not(dbms == "sqlite" && exists("eunomiaConnection"))
  settings <- createDefaultCovariateSettings()
  covariateData <- getDbCovariateData(
    connectionDetails = eunomiaConnectionDetails,
    cdmDatabaseSchema = eunomiaCdmDatabaseSchema,
    cohortDatabaseSchema = eunomiaOhdsiDatabaseSchema,
    cohortIds = c(1),
    covariateSettings = settings,
    aggregated = FALSE
  )

  sumOut <- summary(covariateData)
  Andromeda::close(covariateData)
  expect_equal(sumOut$metaData$cohortIds, 1L)
})

test_that("test filtering of covariates based on minCharacterizationMean", {
  skip_on_cran()
  skip_if_not(dbms == "sqlite" && exists("eunomiaConnection"))
  settings <- createDefaultCovariateSettings()
  covariateData <- getDbCovariateData(
    connectionDetails = eunomiaConnectionDetails,
    cdmDatabaseSchema = eunomiaCdmDatabaseSchema,
    cohortDatabaseSchema = eunomiaOhdsiDatabaseSchema,
    cohortIds = c(1),
    covariateSettings = settings,
    aggregated = TRUE,
    minCharacterizationMean = 0
  )
  nCovariates <- covariateData$covariates %>%
    collect() %>%
    nrow()

  minCharMeanValue <- 0.02
  covariateDataFiltered <- getDbCovariateData(
    connectionDetails = eunomiaConnectionDetails,
    cdmDatabaseSchema = eunomiaCdmDatabaseSchema,
    cohortDatabaseSchema = eunomiaOhdsiDatabaseSchema,
    cohortIds = c(1),
    covariateSettings = settings,
    aggregated = TRUE,
    minCharacterizationMean = minCharMeanValue
  )
  nCovariatesFiltered <- covariateDataFiltered$covariates %>%
    collect() %>%
    nrow()

  expect_true(nCovariatesFiltered < nCovariates)
  expect_true(covariateDataFiltered$covariates %>% pull(averageValue) %>% min() >= minCharMeanValue)
})

test_that("test loadCovariateData", {
  expect_error(loadCovariateData("errorPath"))
})

test_that("Test exit/warning conditions", {
  # Empty Directory test
  tempDir <- tempdir()
  expect_error(loadCovariateData(file = tempDir))
  on.exit(unlink(tempDir))

  # ReadOnly parameter depreciated
  cvData <- FeatureExtraction::createEmptyCovariateData(cohortIds = 1, aggregated = FALSE, temporal = FALSE)
  tempFile <- tempfile()
  tempFileName <- paste0(tempFile, ".zip")
  saveCovariateData(cvData, tempFileName)
  expect_warning(loadCovariateData(file = tempFileName, readOnly = TRUE))
  on.exit(unlink(tempFileName))
  on.exit(rm(cvData))
})

test_that("Test show method", {
  cvData <- FeatureExtraction::createEmptyCovariateData(cohortIds = c(1, 2), aggregated = FALSE, temporal = FALSE)
  expect_invisible(show(cvData))
  on.exit(rm(cvData))
})

test_that("getDbCovariateData cohortId warning", {
  skip_on_cran()
  skip_if_not(dbms == "sqlite" && exists("eunomiaConnection"))
  settings <- createDefaultCovariateSettings()
  expect_warning(getDbCovariateData(
    connectionDetails = eunomiaConnectionDetails,
    cdmDatabaseSchema = eunomiaCdmDatabaseSchema,
    cohortDatabaseSchema = eunomiaOhdsiDatabaseSchema,
    cohortId = c(1),
    covariateSettings = settings,
    aggregated = FALSE
  ), "cohortId argument has been deprecated, please use cohortIds")
})

test_that("getDbCovariateData settings list - check metaData", {
  skip_on_cran()
  skip_if_not(dbms == "sqlite" && exists("eunomiaConnection"))
  looCovSet <- FeatureExtraction:::.createLooCovariateSettings(useLengthOfObs = TRUE)
  covariateSettingsList <- list(looCovSet, looCovSet)
  covariateData <- getDbCovariateData(
    connection = eunomiaConnection,
    cdmDatabaseSchema = eunomiaCdmDatabaseSchema,
    cohortTable = "cohort",
    cohortIds = c(-1),
    covariateSettings = covariateSettingsList
  )
  metaData <- attr(covariateData, "metaData")
  expect_true("sql" %in% names(metaData))
  expect_equal(class(metaData$sql), "list")
  expect_equal(length(metaData$sql), 1)
  expect_equal(length(metaData$sql[[1]]), 2)
})